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Specify an ecological inference problem

Source: R/ei_spec.R
ei_spec.Rd

Uses tidy-select syntax to specify outcomes, predictors, and covariates. The result of this function can be passed directly into ei_ridge() or ei_riesz(), or plotted with plot().

Usage

ei_spec(data, predictors, outcome, total, covariates = NULL, strip = FALSE)

# S3 method for class 'ei_spec'
weights(object, normalize = TRUE, ...)

Arguments

data

A data frame.

predictors

<tidy-select> Predictor variables. This is the x variable in ecological regression that is of primary interest. For example, the columns containing the percentage of each racial group.

outcome

<tidy-select> Outcome variables. This is the y variable in ecological regression that is of primary interest. For example, the columns containing the percentage of votes for each party.

total

<tidy-select> A variable containing the total number of observations in each aggregate unit. For example, the column containing the total number of voters. Required by default.

covariates

<tidy-select> Covariates.

strip

Whether to strip common prefixes from column names within each group. For example, columns named vap_white, vap_black, and vap_hisp would be renamed white, black and other in the model and output.

object

An ei_spec object.

normalize

If TRUE, normalize the totals to have mean 1.

...

Additional arguments (ignored).

Value

An ei_spec object, which is a data frame with additional attributes recording predictors, outcomes, total, and covariates.

Details

The function is lightweight and does not perform any checking of the arguments, bounds, sum constraints, etc. All of these checks are performed by functions that use ei_spec objects.

Methods (by generic)

  • weights(ei_spec): Extract the totals from a specification

Examples

data(elec_1968)
ei_spec(elec_1968, vap_white:vap_other, pres_dem_hum:pres_abs, pres_total)
#> EI Specification
#> • Predictors: `vap_white`, `vap_black`, and `vap_other`
#> • Outcome: `pres_dem_hum`, `pres_rep_nix`, `pres_ind_wal`, and `pres_abs`
#> • Covariates: none
#> # A tibble: 1,143 × 7
#>   vap_white vap_black vap_other pres_dem_hum pres_rep_nix pres_ind_wal pres_abs
#>       <dbl>     <dbl>     <dbl>        <dbl>        <dbl>        <dbl>    <dbl>
#> 1     0.761    0.237   0.00173        0.199        0.0773        0.711  0.0122 
#> 2     0.860    0.137   0.00306        0.105        0.115         0.764  0.0161 
#> 3     0.610    0.389   0.000808       0.242        0.0489        0.687  0.0218 
#> 4     0.783    0.216   0.00106        0.141        0.0571        0.799  0.00290
#> 5     0.981    0.0181  0.000757       0.0375       0.222         0.727  0.0134 
#> # ℹ 1,138 more rows